CrowdANALYTIX's Data Science Competition
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Submissions All Feb 12, 2018
tennis_data Correct files Feb 23, 2018
.DS_Store All Feb 12, 2018
10 Classifier Showdown.ipynb Cleaned 10 Classifier Showdown Feb 23, 2018
ANN.ipynb All Feb 12, 2018
Data Exploration.ipynb All Feb 12, 2018
Ensemble.Rmd All Feb 12, 2018
Ensemble.ipynb All Feb 12, 2018
Ensemble.nb.html All Feb 12, 2018
Gradient Boosting.ipynb Cleaned XGBoost File Feb 22, 2018
H2O.ipynb H2O Feb 12, 2018
Neural Network.ipynb Cleaned Neural Network Feb 23, 2018
New ANN.ipynb All Feb 12, 2018 Create Feb 23, 2018 All Feb 12, 2018


CrowdANALYTIX's Data Science Competition

Predicting How Points End in Tennis was a competition hosted on CrowdAnalytix. This was also my first data science competition, so I spent a lot of time learning and exploring different algorithms to get a feel about how each algorithm worked. The goal of the compeittion was to predict how the point ended: winner, unforced error, or forced error. The model I settled on was XGBoost, which is the go-to algorithm among Kagglers.